On the effects of dephasing due to local gradients in diffusion tensor imaging experiments: relevance for diffusion tensor imaging fiber phantoms.

The effect of susceptibility differences between fluid and fibers on the properties of DTI fiber phantoms was investigated. Thereto, machine-made, easily producible and inexpensive DTI fiber phantoms were constructed by winding polyamide fibers of 15 microm diameter around a circular acrylic glass spindle. The achieved fractional anisotropy was 0.78+/-0.02. It is shown by phantom measurements and Monte Carlo simulations that the transversal relaxation time T(2) strongly depends on the angle between the fibers and the B(0) field if the susceptibilities of the fibers and fluid are not identical. In the phantoms, the measured T(2) time at 3 T decreased by 60% for fibers running perpendicular to B(0). Monte Carlo simulations confirmed this result and revealed that the exact relaxation time depends strongly on the exact packing of the fibers. In the phantoms, the measured diffusion was independent of fiber orientation. Monte Carlo simulations revealed that the measured diffusion strongly depends on the exact fiber packing and that field strength and -orientation dependencies of measured diffusion may be minimal for hexagonal packing while the diffusion can be underestimated by more than 50% for cubic packing at 3 T. To overcome these effects, the susceptibilities of fibers and fluid were matched using an aqueous sodium chloride solution (83 g NaCl per kilogram of water). This enables an orientation independent and reliable use of DTI phantoms for evaluation purposes.

[1]  Heinz-Otto Peitgen,et al.  Diffusion tensor imaging in primary brain tumors: Reproducible quantitative analysis of corpus callosum infiltration and contralateral involvement using a probabilistic mixture model , 2006, NeuroImage.

[2]  T. Benner,et al.  Reducing motion artefacts in diffusion-weighted MRI of the brain: efficacy of navigator echo correction and pulse triggering , 2000, Neuroradiology.

[3]  S. Heiland,et al.  Noise correction for the exact determination of apparent diffusion coefficients at low SNR , 2001, Magnetic resonance in medicine.

[4]  R. Deriche,et al.  Apparent diffusion coefficients from high angular resolution diffusion imaging: Estimation and applications , 2006, Magnetic resonance in medicine.

[5]  A. Connelly,et al.  Anisotropic noise propagation in diffusion tensor MRI sampling schemes , 2003, Magnetic resonance in medicine.

[6]  Yi-Qiao Song,et al.  Multiple echo diffusion tensor acquisition technique. , 2006, Magnetic resonance imaging.

[7]  Derek K. Jones,et al.  Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI , 2003, Magnetic resonance in medicine.

[8]  E. Haacke,et al.  Theory of NMR signal behavior in magnetically inhomogeneous tissues: The static dephasing regime , 1994, Magnetic resonance in medicine.

[9]  J. Schenck The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. , 1996, Medical physics.

[10]  S. Skare,et al.  Noise considerations in the determination of diffusion tensor anisotropy. , 2000, Magnetic resonance imaging.

[11]  C. Beaulieu,et al.  Anisotropic diffusion of metabolites in peripheral nerve using diffusion weighted magnetic resonance spectroscopy at ultra-high field. , 2007, Journal of magnetic resonance.

[12]  D. Tuch Q‐ball imaging , 2004, Magnetic resonance in medicine.

[13]  D. LeBihan,et al.  Validation of q-ball imaging with a diffusion fibre-crossing phantom on a clinical scanner , 2005, Philosophical Transactions of the Royal Society B: Biological Sciences.

[14]  R. C. Weast CRC Handbook of Chemistry and Physics , 1973 .

[15]  J. Gore,et al.  Intravascular susceptibility contrast mechanisms in tissues , 1994, Magnetic resonance in medicine.

[16]  John C. Gore,et al.  Effects of susceptibility variations on NMR measurements of diffusion , 1991 .

[17]  Bengt Jönsson,et al.  Restricted Diffusion in Cylindrical Geometry , 1995 .

[18]  Ignace Lemahieu,et al.  Quantitative Validation of White Matter Fiber Tractography by use of an Anatomically Realistic Synthetic Diffusion Tensor Phantom , 2006 .

[19]  D L Parker,et al.  Comparison of gradient encoding schemes for diffusion‐tensor MRI , 2001, Journal of magnetic resonance imaging : JMRI.

[20]  A. Anderson,et al.  Effects of cord motion on diffusion imaging of the spinal cord , 2006, Magnetic resonance in medicine.

[21]  Ching Yao,et al.  Validation of diffusion spectrum magnetic resonance imaging with manganese-enhanced rat optic tracts and ex vivo phantoms , 2003, NeuroImage.

[22]  Won-Jin Moon,et al.  How does distortion correction correlate with anisotropic indices? A diffusion tensor imaging study. , 2006, Magnetic resonance imaging.

[23]  A. Anderson Theoretical analysis of the effects of noise on diffusion tensor imaging , 2001, Magnetic resonance in medicine.

[24]  R Mark Henkelman,et al.  Orientational diffusion reflects fiber structure within a voxel , 2002, Magnetic resonance in medicine.

[25]  V. Kiselev,et al.  Effect of magnetic field gradients induced by microvasculature on NMR measurements of molecular self-diffusion in biological tissues. , 2004, Journal of magnetic resonance.

[26]  W R Reinus,et al.  Quantitation of T2′ anisotropic effects on magnetic resonance bone mineral density measurement , 1997, Magnetic resonance in medicine.

[27]  T E Conturo,et al.  Diffusion MRI: Precision, accuracy and flow effects , 1995, NMR in biomedicine.

[28]  C. Thomsen,et al.  Theoretical and experimental evaluation of phase‐dispersion effects caused by brain motion in diffusion and perfusion MR imaging , 1996, Journal of magnetic resonance imaging : JMRI.

[29]  S Skare,et al.  Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. , 2000, Journal of magnetic resonance.

[30]  E. Achten,et al.  Simulation and experimental verification of the diffusion in an anisotropic fiber phantom. , 2008, Journal of magnetic resonance.

[31]  Nathan Yanasak,et al.  Use of capillaries in the construction of an MRI phantom for the assessment of diffusion tensor imaging: demonstration of performance. , 2006, Magnetic resonance imaging.

[32]  Derek K. Jones,et al.  The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study † , 2004, Magnetic resonance in medicine.

[33]  Osamu Abe,et al.  Flexible ex vivo phantoms for validation of diffusion tensor tractography on a clinical scanner , 2006, Radiation Medicine.

[34]  Derek K. Jones,et al.  “Squashing peanuts and smashing pumpkins”: How noise distorts diffusion‐weighted MR data , 2004, Magnetic resonance in medicine.